MachineHack Winners: How This Team Of Data Scientists Worked On Making Roads Safer With Intel OpenVINO Toolkit

MachineHack recently concluded its ‘Making Autonomous Vehicles Safer For Humans’ Hackathon by Intel. Analytics India Magazine spoke to the members of the winning team to know about their data science journey and how they solved the problem.

Manisha Biswas

Journey In Data Science

Biswas started her journey into data science after beginning her career as a software engineer. Her interest in data science and machine learning made her want to learn the software. She said, “Initially it was tough as I had to balance my full-time job and study late-night. I challenged myself to learn and also share my experience by writing technical blogs.”

This led to her co-authoring a book with Apress Publications titled Reinforcement Learning With Open AI, TensorFlow and Keras Using Python. This book has been sold over 34,000 copies, leading to a second book known as Neural Networks in Unity.

Solving The MachineHack Problem

One of the biggest steps, according to Biswas, was gaining insights and identifying the problem statement. This, along with teamwork, led to the implementation of Faster RCNN with re-annotations of the included datasets.

In addition to this, they also solved the problems in different ways, leading to Pytorch being chosen for the final solution. This resulted in the solutions being created with inference on the Intel OpenVINO Toolkit.

Overall MachineHack Experience

Biswas stated that the overall experience was great, with learning regarding problem statement and healthy competition being big factors. She stated that the leaderboard was also a very helpful addition, ensuring that participants were able to see where they stood.

Subhashis Banerjee

Journey In Data Science

Banerjee is an active participant in the data science space, having been working in the area of Biomedical data science for the last five years. He is currently in the process of completing his PhD thesis, which is focused on brain cancer. Currently, he is working as a Senior Research Fellow at Machine Intelligence Unit, Indian Statistical Institute (ISI) in Kolkata.

He first started learning data science about 5 years ago when he joined ISI. Subashish stated, “The exposure I received in the field of machine learning, data mining, statistics and analytical thinking at ISI during the PhD coursework has motivated me to choose the above mentioned challenging research area for my PhD.”

Solving The MachineHack Problem

Banerjee described the two approaches the team took up to solve the hackathon problem. First, they trained the FasterRCNN model on the IDD dataset for 15 classes. For the second approach we used an ensemble of three pre-trained models YOLOv3, RetinaNET, TinyVOLOv3, trained on the COCO dataset.

Banerjee also revealed that these projects were created using Intel-optimized Python. The experiments were run on the Intel AI DevCloud platform and used a cluster of Intel Xeon Scalable processors.

Overall MachineHack Experience

Banerjee stated on his MachineHack experience,

“It was a really challenging but an absolutely incredible experience from the time I started this hackathon. User Interface in the hackathon website is appreciable. Willing to participate in this type of hackathons more in the future.”

Prajjwal Bhargava

Journey In Data Science

Even though Bhargava does not specialise in data science, he utilized EDA and other processes to make the problem usable for the algorithm of his choice. He mostly specializes in vision, NLP and reinforcement learning.

Having started his journey in the second year of his undergraduate studies, Bhargava grew by taking online courses, reading books and implementing what he learnt in those texts.

Solving The MachineHack Problem

Bhargava treated understanding the data as a key process in finding the solution. This is due to the fact that the data must be carefully analyzed, and the preliminary issues, such as an imbalance in classes must be removed. Bhargava’s second step was to create a proper mapping of images and annotations to remove the possibility of any false ground truth.

He elaborated, “From there, it involved creating the network (FasterRCNN with ResNet50 pre-trained on COCO), standard data augmentation and loss for training. We trained the network on non HD images initially and then trained on HQ images since inference was being done on this type of images.”